Intention recognition method and device based on multi-parameter K-means algorithm and electronic equipment
A k-means algorithm and recognition method technology, applied in character and pattern recognition, computing, computer parts and other directions, to achieve accurate intent classification and identification, improve intent clustering quality, and optimize methods.
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Embodiment 1
[0045] Below, will refer to Figure 1 to Figure 3 An embodiment of the intent recognition method based on the multi-parameter K-means algorithm of the present invention is described.
[0046] figure 1 It is a flowchart of an example of the intention recognition method based on the multi-parameter K-means algorithm of the present invention.
[0047] Such as figure 1 As shown, an intent recognition method based on a multi-parameter K-means algorithm, the method includes the following steps.
[0048] In step S101, a sample data set is established, and the sample data set includes a plurality of semantic vectors converted from dialogue texts converted from speech input by a user when having a dialogue with an intelligent voice robot.
[0049] Step S102, using the K-means algorithm to perform multiple rounds of clustering processing on the sample data set, each round of clustering adopts a different k value, and outputs an initial clustering result, and the k value is the K-mean...
Embodiment 2
[0108] refer to Figure 4 , Figure 5 with Image 6 , the present invention also provides an intention identification device 400 based on a multi-parameter K-means algorithm, which is applied to the identification of user intentions in an intelligent voice robot, including: an establishment module 401 for establishing a sample data set, the sample data set Including a plurality of semantic vectors obtained by dialogue text conversion, the dialogue text is converted from the voice input when the user talks to the intelligent voice robot; the clustering processing module 402 is used to use the K-means algorithm to analyze the sample data set Carry out multiple rounds of clustering processing, each round of clustering adopts a different k value, and output the initial clustering result, the k value is the number of initial center vectors in the K-means algorithm; setting module 403, for Set a fusion strategy, and determine the initial clustering results to be fused, the fusion ...
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